DNA characterization reveals potential operon-unit packaging of extracellular vesicle cargo from a gut bacterial symbiont.

Extracellular vesicles (EVs) are lipid bilayer-bound entities secreted by cells across all domains of life, known to contain a range of components, including protein complexes, RNA, and DNA. Recent studies on microbial extracellular vesicles indicate that these virus-sized nanoparticles, 40-90nm in diameter, readily cross the epithelial barrier and reach systemic circulation, can be detected in tissues throughout the body in mice and that 1mL of plasma from healthy humans contains up to one million bacterial EVs. They have been recently recognized for their biologically functional roles, including modulation of bacterial physiology and host-microbe interactions, hence their gain in the microbiome research community's attention. However, the exact understanding of their functionality is still a subject of active research and debate. Here, we employ long-read DNA sequencing on purified extracellular vesicles from a common mammalian gut symbiont, Parabacteroides goldsteinii, to characterize the genomic component within EV cargos. Our findings challenge the notion of DNA packaging into EVs as a stochastic event. Instead, our data demonstrate that the DNA packaging is non-random. Here, we suggest a novel hypothesis of selective EV-DNA packaging, potentially arranged in operon units, hence providing new insights into our understanding of its genetic makeup and its potential role, underlining the importance of our findings in microbial community dynamics.

Quantifying Shared and Unique Gene Content across 17 Microbial Ecosystems.

Measuring microbial diversity is traditionally based on microbe taxonomy. Here, in contrast, we aimed to quantify heterogeneity in microbial gene content across 14,183 metagenomic samples spanning 17 ecologies, including 6 human associated, 7 nonhuman host associated, and 4 in other nonhuman host environments. In total, we identified 117,629,181 nonredundant genes. The vast majority of genes (66%) occurred in only one sample (i.e., "singletons"). In contrast, we found 1,864 sequences present in every metagenome, but not necessarily every bacterial genome. Additionally, we report data sets of other ecology-associated genes (e.g., abundant in only gut ecosystems) and simultaneously demonstrated that prior microbiome gene catalogs are both incomplete and inaccurately cluster microbial genetic life (e.g., at gene sequence identities that are too restrictive). We provide our results and the sets of environmentally differentiating genes described above at http://www.microbial-genes.bio. IMPORTANCE The amount of shared genetic elements has not been quantified between the human microbiome and other host- and non-host-associated microbiomes. Here, we made a gene catalog of 17 different microbial ecosystems and compared them. We show that most species shared between environment and human gut microbiomes are pathogens and that prior gene catalogs described as "nearly complete" are far from it. Additionally, over two-thirds of all genes only appear in a single sample, and only 1,864 genes (0.001%) are found in all types of metagenomes. These results highlight the large diversity between metagenomes and reveal a new, rare class of genes, those found in every type of metagenome, but not every microbial genome.

Resources to Facilitate Use of the Altered Schaedler Flora (ASF) Mouse Model to Study Microbiome Function.

Animals colonized with a defined microbiota represent useful experimental systems to investigate microbiome function. The altered Schaedler flora (ASF) represents a consortium of eight murine bacterial species that have been used for more than 4 decades where the study of mice with a reduced microbiota is desired. In contrast to germ-free mice, or mice colonized with only one or two species, ASF mice show the normal gut structure and immune system development. To further expand the utility of the ASF, we have developed technical and bioinformatic resources to enable a systems-based analysis of microbiome function using this model. Here, we highlighted four distinct applications of these resources that enable and improve (i) measurements of the abundance of each ASF member by quantitative PCR; (ii) exploration and comparative analysis of ASF genomes and the metabolic pathways they encode that comprise the entire gut microbiome; (iii) global transcriptional profiling to identify genes whose expression responds to environmental changes within the gut; and (iv) discovery of genetic changes resulting from the evolutionary adaptation of the microbiota. These resources were designed to be accessible to a broad community of researchers that, in combination with conventionally-reared mice (i.e., with complex microbiome), should contribute to our understanding of microbiome structure and function. IMPORTANCE Improved experimental systems are needed to advance our understanding of how the gut microbiome influences processes of the mammalian host as well as microbial community structure and function. An approach that is receiving considerable attention is the use of animal models that harbor a stable microbiota of known composition, i.e., defined microbiota, which enables control over an otherwise highly complex and variable feature of mammalian biology. The altered Schaedler flora (ASF) consortium is a well-established defined microbiota model, where mice are stably colonized with 8 distinct murine bacterial species. To take better advantage of the ASF, we established new experimental and bioinformatics resources for researchers to make better use of this model as an experimental system to study microbiome function.

The human microbiome: A coming of age story.

The human microbiome field is coming of age, but it is still defining itself. I can say the same as an investigator who started his career in the early days of this expanding field. This commentary reflects on my Cell Host & Microbe papers along this journey that captured the field's progress.

Systematically assessing microbiome-disease associations identifies drivers of inconsistency in metagenomic research.

Evaluating the relationship between the human gut microbiome and disease requires computing reliable statistical associations. Here, using millions of different association modeling strategies, we evaluated the consistency-or robustness-of microbiome-based disease indicators for 6 prevalent and well-studied phenotypes (across 15 public cohorts and 2,343 individuals). We were able to discriminate between analytically robust versus nonrobust results. In many cases, different models yielded contradictory associations for the same taxon-disease pairing, some showing positive correlations and others negative. When querying a subset of 581 microbe-disease associations that have been previously reported in the literature, 1 out of 3 taxa demonstrated substantial inconsistency in association sign. Notably, >90% of published findings for type 1 diabetes (T1D) and type 2 diabetes (T2D) were particularly nonrobust in this regard. We additionally quantified how potential confounders-sequencing depth, glucose levels, cholesterol, and body mass index, for example-influenced associations, analyzing how these variables affect the ostensible correlation between Faecalibacterium prausnitzii abundance and a healthy gut. Overall, we propose our approach as a method to maximize confidence when prioritizing findings that emerge from microbiome association studies.

Using Cartesian Doubt To Build a Sequencing-Based View of Microbiology.

The technological leap of DNA sequencing generated a tension between modern metagenomics and historical microbiology. We are forcibly harmonizing the output of a modern tool with centuries of experimental knowledge derived from culture-based microbiology. As a thought experiment, we borrow the notion of Cartesian doubt from philosopher Rene Descartes, who used doubt to build a philosophical framework from his incorrigible statement that "I think therefore I am." We aim to cast away preconceived notions and conceptualize microorganisms through the lens of metagenomic sequencing alone. Specifically, we propose funding and building analysis and engineering methods that neither search for nor rely on the assumption of independent genomes bound by lipid barriers containing discrete functional roles and taxonomies. We propose that a view of microbial communities based in sequencing will engender novel insights into metagenomic structure and may capture functional biology not reflected within the current paradigm.

Leveraging vibration of effects analysis for robust discovery in observational biomedical data science.

Hypothesis generation in observational, biomedical data science often starts with computing an association or identifying the statistical relationship between a dependent and an independent variable. However, the outcome of this process depends fundamentally on modeling strategy, with differing strategies generating what can be called "vibration of effects" (VoE). VoE is defined by variation in associations that often lead to contradictory results. Here, we present a computational tool capable of modeling VoE in biomedical data by fitting millions of different models and comparing their output. We execute a VoE analysis on a series of widely reported associations (e.g., carrot intake associated with eyesight) with an extended additional focus on lifestyle exposures (e.g., physical activity) and components of the Framingham Risk Score for cardiovascular health (e.g., blood pressure). We leveraged our tool for potential confounder identification, investigating what adjusting variables are responsible for conflicting models. We propose modeling VoE as a critical step in navigating discovery in observational data, discerning robust associations, and cataloging adjusting variables that impact model output.

Gene-level metagenomic architectures across diseases yield high-resolution microbiome diagnostic indicators.

We propose microbiome disease "architectures": linking >1 million microbial features (species, pathways, and genes) to 7 host phenotypes from 13 cohorts using a pipeline designed to identify associations that are robust to analytical model choice. Here, we quantify conservation and heterogeneity in microbiome-disease associations, using gene-level analysis to identify strain-specific, cross-disease, positive and negative associations. We find coronary artery disease, inflammatory bowel diseases, and liver cirrhosis to share gene-level signatures ascribed to the Streptococcus genus. Type 2 diabetes, by comparison, has a distinct metagenomic signature not linked to any one specific species or genus. We additionally find that at the species-level, the prior-reported connection between Solobacterium moorei and colorectal cancer is not consistently identified across models-however, our gene-level analysis unveils a group of robust, strain-specific gene associations. Finally, we validate our findings regarding colorectal cancer and inflammatory bowel diseases in independent cohorts and identify that features inversely associated with disease tend to be less reproducible than features enriched in disease. Overall, our work is not only a step towards gene-based, cross-disease microbiome diagnostic indicators, but it also illuminates the nuances of the genetic architecture of the human microbiome, including tension between gene- and species-level associations.

Reconstruction of ancient microbial genomes from the human gut.

Loss of gut microbial diversity1-6 in industrial populations is associated with chronic diseases7, underscoring the importance of studying our ancestral gut microbiome. However, relatively little is known about the composition of pre-industrial gut microbiomes. Here we performed a large-scale de novo assembly of microbial genomes from palaeofaeces. From eight authenticated human palaeofaeces samples (1,000-2,000 years old) with well-preserved DNA from southwestern USA and Mexico, we reconstructed 498 medium- and high-quality microbial genomes. Among the 181 genomes with the strongest evidence of being ancient and of human gut origin, 39% represent previously undescribed species-level genome bins. Tip dating suggests an approximate diversification timeline for the key human symbiont Methanobrevibacter smithii. In comparison to 789 present-day human gut microbiome samples from eight countries, the palaeofaeces samples are more similar to non-industrialized than industrialized human gut microbiomes. Functional profiling of the palaeofaeces samples reveals a markedly lower abundance of antibiotic-resistance and mucin-degrading genes, as well as enrichment of mobile genetic elements relative to industrial gut microbiomes. This study facilitates the discovery and characterization of previously undescribed gut microorganisms from ancient microbiomes and the investigation of the evolutionary history of the human gut microbiota through genome reconstruction from palaeofaeces.

Circulating short and medium chain fatty acids are associated with normoalbuminuria in type 1 diabetes of long duration.

A substantial number of subjects with Type 1 Diabetes (T1D) of long duration never develop albuminuria or renal function impairment, yet the underlying protective mechanisms remain unknown. Therefore, our study included 308 Joslin Kidney Study subjects who had T1D of long duration (median: 24 years), maintained normal renal function and had either normoalbuminuria or a broad range of albuminuria within the 2 years preceding the metabolomic determinations. Serum samples were subjected to global metabolomic profiling. 352 metabolites were detected in at least 80% of the study population. In the logistic analyses adjusted for multiple testing (Bonferroni corrected α = 0.000028), we identified 38 metabolites associated with persistent normoalbuminuria independently from clinical covariates. Protective metabolites were enriched in Medium Chain Fatty Acids (MCFAs) and in Short Chain Fatty Acids (SCFAs) and particularly involved odd-numbered and dicarboxylate Fatty Acids. One quartile change of nonanoate, the top protective MCFA, was associated with high odds of having persistent normoalbuminuria (OR (95% CI) 0.14 (0.09, 0.23); p < 10-12). Multivariable Random Forest analysis concordantly indicated to MCFAs as effective classifiers. Associations of the relevant Fatty Acids with albuminuria seemed to parallel associations with tubular biomarkers. Our findings suggest that MCFAs and SCFAs contribute to the metabolic processes underlying protection against albuminuria development in T1D that are independent from mechanisms associated with changes in renal function.

Mechanisms of microbial-neuronal interactions in pain and nociception.

Nociceptor sensory neurons innervate barrier tissues that are constantly exposed to microbial stimuli. During infection, pathogenic microorganisms can breach barrier surfaces and produce pain by directly activating nociceptors. Microorganisms that live in symbiotic relationships with their hosts, commensals and mutualists, have also been associated with pain, but the molecular mechanisms of how symbionts act on nociceptor neurons to modulate pain remain largely unknown. In this review, we will discuss the known molecular mechanisms of how microbes directly interact with sensory afferent neurons affecting nociception in the gut, skin and lungs. We will touch on how bacterial, viral and fungal pathogens signal to the host to inflict or suppress pain. We will also discuss recent studies examining how gut symbionts affect pain. Specifically, we will discuss how gut symbionts may interact with sensory afferent neurons either directly, through secretion of metabolites or neurotransmitters, or indirectly,through first signaling to epithelial cells or immune cells, to regulate visceral, neuropathic and inflammatory pain. While this area of research is still in its infancy, more mechanistic studies to examine microbial-sensory neuron crosstalk in nociception may allow us to develop new therapies for the treatment of acute and chronic pain.

A systematic machine learning and data type comparison yields metagenomic predictors of infant age, sex, breastfeeding, antibiotic usage, country of origin, and delivery type.

The microbiome is a new frontier for building predictors of human phenotypes. However, machine learning in the microbiome is fraught with issues of reproducibility, driven in large part by the wide range of analytic models and metagenomic data types available. We aimed to build robust metagenomic predictors of host phenotype by comparing prediction performances and biological interpretation across 8 machine learning methods and 4 different types of metagenomic data. Using 1,570 samples from 300 infants, we fit 7,865 models for 6 host phenotypes. We demonstrate the dependence of accuracy on algorithm choice and feature definition in microbiome data and propose a framework for building microbiome-derived indicators of host phenotype. We additionally identify biological features predictive of age, sex, breastfeeding status, historical antibiotic usage, country of origin, and delivery type. Our complete results can be viewed at http://apps.chiragjpgroup.org/ubiome_predictions/.

The Landscape of Genetic Content in the Gut and Oral Human Microbiome.

Despite substantial interest in the species diversity of the human microbiome and its role in disease, the scale of its genetic diversity, which is fundamental to deciphering human-microbe interactions, has not been quantified. Here, we conducted a cross-study meta-analysis of metagenomes from two human body niches, the mouth and gut, covering 3,655 samples from 13 studies. We found staggering genetic heterogeneity in the dataset, identifying a total of 45,666,334 non-redundant genes (23,961,508 oral and 22,254,436 gut) at the 95% identity level. Fifty percent of all genes were "singletons," or unique to a single metagenomic sample. Singletons were enriched for different functions (compared with non-singletons) and arose from sub-population-specific microbial strains. Overall, these results provide potential bases for the unexplained heterogeneity observed in microbiome-derived human phenotypes. One the basis of these data, we built a resource, which can be accessed at https://microbial-genes.bio.

Predictive metabolomic profiling of microbial communities using amplicon or metagenomic sequences.

Microbial community metabolomics, particularly in the human gut, are beginning to provide a new route to identify functions and ecology disrupted in disease. However, these data can be costly and difficult to obtain at scale, while amplicon or shotgun metagenomic sequencing data are readily available for populations of many thousands. Here, we describe a computational approach to predict potentially unobserved metabolites in new microbial communities, given a model trained on paired metabolomes and metagenomes from the environment of interest. Focusing on two independent human gut microbiome datasets, we demonstrate that our framework successfully recovers community metabolic trends for more than 50% of associated metabolites. Similar accuracy is maintained using amplicon profiles of coral-associated, murine gut, and human vaginal microbiomes. We also provide an expected performance score to guide application of the model in new samples. Our results thus demonstrate that this 'predictive metabolomic' approach can aid in experimental design and provide useful insights into the thousands of community profiles for which only metagenomes are currently available.

Meta-omics analysis of elite athletes identifies a performance-enhancing microbe that functions via lactate metabolism.

The human gut microbiome is linked to many states of human health and disease1. The metabolic repertoire of the gut microbiome is vast, but the health implications of these bacterial pathways are poorly understood. In this study, we identify a link between members of the genus Veillonella and exercise performance. We observed an increase in Veillonella relative abundance in marathon runners postmarathon and isolated a strain of Veillonella atypica from stool samples. Inoculation of this strain into mice significantly increased exhaustive treadmill run time. Veillonella utilize lactate as their sole carbon source, which prompted us to perform a shotgun metagenomic analysis in a cohort of elite athletes, finding that every gene in a major pathway metabolizing lactate to propionate is at higher relative abundance postexercise. Using 13C3-labeled lactate in mice, we demonstrate that serum lactate crosses the epithelial barrier into the lumen of the gut. We also show that intrarectal instillation of propionate is sufficient to reproduce the increased treadmill run time performance observed with V. atypica gavage. Taken together, these studies reveal that V. atypica improves run time via its metabolic conversion of exercise-induced lactate into propionate, thereby identifying a natural, microbiome-encoded enzymatic process that enhances athletic performance.

Bacteroides-Derived Sphingolipids Are Critical for Maintaining Intestinal Homeostasis and Symbiosis.

Sphingolipids are structural membrane components and important eukaryotic signaling molecules. Sphingolipids regulate inflammation and immunity and were recently identified as the most differentially abundant metabolite in stool from inflammatory bowel disease (IBD) patients. Commensal bacteria from the Bacteroidetes phylum also produce sphingolipids, but the impact of these metabolites on host pathways is largely uncharacterized. To determine whether bacterial sphingolipids modulate intestinal health, we colonized germ-free mice with a sphingolipid-deficient Bacteroides thetaiotaomicron strain. A lack of Bacteroides-derived sphingolipids resulted in intestinal inflammation and altered host ceramide pools in mice. Using lipidomic analysis, we described a sphingolipid biosynthesis pathway and revealed a variety of Bacteroides-derived sphingolipids including ceramide phosphoinositol and deoxy-sphingolipids. Annotating Bacteroides sphingolipids in an IBD metabolomic dataset revealed lower abundances in IBD and negative correlations with inflammation and host sphingolipid production. These data highlight the role of bacterial sphingolipids in maintaining homeostasis and symbiosis in the gut.

A perfect storm: Genetics and anticommensal antibodies shore up type 1 diabetes.

HLA haplotypes in conjunction with serum anticommensal antibody responses are predictive of type 1 diabetes progression. See related Research Article by Paun et al.

Fusobacterium nucleatum in Colorectal Cancer Relates to Immune Response Differentially by Tumor Microsatellite Instability Status.

The presence of Fusobacterium nucleatum (F. nucleatum) in colorectal carcinoma tissue has been associated with microsatellite instability (MSI), lower-level T-cell infiltrates, and poor clinical outcomes. Considering differences in the tumor-immune microenvironment between MSI-high and non-MSI-high carcinomas, we hypothesized that the association of F. nucleatum with immune response might differ by tumor MSI status. Using samples from 1,041 rectal and colon cancer patients within the Nurses' Health Study and Health Professionals Follow-up Study, we measured F. nucleatum DNA in tumor tissue by a quantitative polymerase chain reaction assay. Multivariable logistic regression models were used to examine the association between F. nucleatum status and histopathologic lymphocytic reactions or density of CD3+ cells, CD8+ cells, CD45RO (PTPRC)+ cells, or FOXP3+ cells in strata of tumor MSI status. We adjusted for potential confounders, including CpG island methylator phenotype; LINE-1 methylation; and KRAS, BRAF, and PIK3CA mutations. The association of F. nucleatum with tumor-infiltrating lymphocytes (TIL) and intratumoral periglandular reaction differed by tumor MSI status (P interaction = 0.002). The presence of F. nucleatum was negatively associated with TIL in MSI-high tumors [multivariable odds ratio (OR), 0.45; 95% confidence interval (CI), 0.22-0.92], but positively associated with TIL in non-MSI-high tumors (multivariable OR 1.91; 95% CI, 1.12-3.25). No significant differential association was observed for peritumoral lymphocytic reaction, Crohn-like lymphoid reaction, or T-cell densities. In conclusion, the association of F. nucleatum with immune response to colorectal carcinoma differs by tumor MSI status, suggesting that F. nucleatum and MSI status interact to affect antitumor immune reactions. Cancer Immunol Res; 6(11); 1327-36. ©2018 AACR See related Spotlight on p. 1290.